假设我想计算下一个数据帧的“dat_1”到“dat_3”列的平均值，标准偏差和n （非NA值的数量），按因子“fac_1”和“fac_2”分组，例如可以从结果中访问每个统计信息（或函数）的单独数据帧
set.seed(1) df <- data.frame("fac_1" = c(rep("a", 5), rep("b", 4)), "fac_2" = c("x", "x", "y","y", "y", "y", "x", "x", "x"), "dat_1" = c(floor(runif(3, 0, 10)), NA, floor(runif(5, 0, 10))), "dat_2" = floor(runif(9, 10, 20)), "dat_3" = floor(runif(9, 20, 30)))这可以使用plyr一次实现一个功能
ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { colMeans(x[, 3:5], na.rm = T) } ) # mean ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { psych::SD(x[, 3:5], na.rm = T) } ) # standrd deviation -- note uses SD from the 'psych' package ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { colSums(!is.na(x[, 3:5])) } ) # number of non-NA values但是当使用多个函数时，这变得很麻烦，特别是当必须改变感兴趣的因子和列时。 我想知道是否有另一种选择（或许是单线）。
聚合工作
aggregate( x = df[, c(3:5)], by = df[, c(1,2)], FUN = function(x) c(n = length( !is.na(x) ), mean = mean(x, na.rm = T), sd = sd(x, na.rm = T) ) )但“分解”结果（分成每个统计数据的单独数据框）变得尴尬。
最近我遇到了dplyr 。 以下似乎有效
df %>% group_by(fac_1, fac_2) %>% summarise_each(funs(n = length( !is.na(.) ), mean(., na.rm = TRUE), sd(., na.rm = TRUE) )) # using dplyr但是我希望能够将因子粘贴到group_by() ，而我却找不到这样做的方法。
任何帮助或想法？ 谢谢
Suppose I'd like to calculate the mean, standard deviation, and n (number of non-NA values) for columns "dat_1" to "dat_3" of the following dataframe, grouped by the factors "fac_1" and "fac_2", such that separate dataframes for each statistic (or function) can be accessed from the result
set.seed(1) df <- data.frame("fac_1" = c(rep("a", 5), rep("b", 4)), "fac_2" = c("x", "x", "y","y", "y", "y", "x", "x", "x"), "dat_1" = c(floor(runif(3, 0, 10)), NA, floor(runif(5, 0, 10))), "dat_2" = floor(runif(9, 10, 20)), "dat_3" = floor(runif(9, 20, 30)))This can be achieved one function at a time using plyr, as such
ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { colMeans(x[, 3:5], na.rm = T) } ) # mean ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { psych::SD(x[, 3:5], na.rm = T) } ) # standrd deviation -- note uses SD from the 'psych' package ddply(.data = df, .variables = .(df$fac_1, df$fac_2), .fun = function(x) { colSums(!is.na(x[, 3:5])) } ) # number of non-NA valuesbut this becomes cumbersome when using multiple functions, especially when factors and columns of interest must be changed. I'm wondering if there's an alternative (a one-liner, perhaps).
Aggregate works
aggregate( x = df[, c(3:5)], by = df[, c(1,2)], FUN = function(x) c(n = length( !is.na(x) ), mean = mean(x, na.rm = T), sd = sd(x, na.rm = T) ) )but 'disaggregating' the result (into separate dataframes for each statistic) becomes awkward.
Recently I've come across dplyr. The following seems to work
df %>% group_by(fac_1, fac_2) %>% summarise_each(funs(n = length( !is.na(.) ), mean(., na.rm = TRUE), sd(., na.rm = TRUE) )) # using dplyrhowever I'd like to be able to paste factors into group_by(), and I've not found a way to do so.
Any help or ideas? Thanks
最满意答案
将向量或列表传递给dplyr函数可能很棘手（请参阅此插图。 ）简而言之，它涉及添加额外的下划线，使用函数的标准求值版本，然后将向量或列表传递给.dots参数。
factorsToSummarise <- c('fac_1', 'fac_2') # extra underscore # | df %>% # v group_by_(.dots = factorsToSummarise) %>% summarise_each(funs(n = length( !is.na(.) ), mean(., na.rm = TRUE), sd(., na.rm = TRUE) )) # using dplyrPassing vectors or lists to dplyr functions can be tricky (see this vignette.) In short, it involves adding an additional underscore, to use the standard evaluation version of a function, and then passing a vector or list to the .dots argument.
factorsToSummarise <- c('fac_1', 'fac_2') # extra underscore # | df %>% # v group_by_(.dots = factorsToSummarise) %>% summarise_each(funs(n = length( !is.na(.) ), mean(., na.rm = TRUE), sd(., na.rm = TRUE) )) # using dplyr更多推荐
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